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[Preprint]. 2020 Nov 10:2020.11.06.20227165.
doi: 10.1101/2020.11.06.20227165.

ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms

Juan Zhao  1 , Monika E Grabowska  2 , Vern Eric Kerchberger  3   1 , Joshua C Smith  1 , H Nur Eken  4 , QiPing Feng  5 , Josh F Peterson  1 , S Trent Rosenbloom  1 , Kevin B Johnson  1 , Wei-Qi Wei  1
Affiliations

ConceptWAS: a high-throughput method for early identification of COVID-19 presenting symptoms

Juan Zhao et al. medRxiv. .

Update in

Abstract

Objective: Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach - Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.

Methods: Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identifying specific COVID-19 symptoms.

Results: We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020. We found 68 clinical concepts significantly associated with COVID-19. We identified symptoms associated with increasing risk of COVID-19, including "absent sense of smell" (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21-7.50), "fever" (OR = 1.43, 95% CI = 1.28-1.59), "with cough fever" (OR = 2.29, 95% CI = 1.75-2.96), and "ageusia" (OR = 5.18, 95% CI = 3.02-8.58). Using ConceptWAS, we were able to detect loss sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC).

Conclusion: ConceptWAS is a high-throughput approach for exploring specific symptoms of a disease like COVID-19, with a promise for enabling EHR-powered early disease manifestations identification.

Keywords: COVID-19; EHR; Natural language processing.

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Conflict of interest statement

Competing interests The authors have no competing interests to declare.

Figures

Figure 1.
Figure 1.
Volcano plot of a ConceptWAS scan for 19, 692 patients that included COVID-19-positive group (cases) and negative group (controls). The points are colored by the semantic type of the concepts. Selected associations related to signs, symptoms, or diseases/syndromes are labeled. The volcano plot indicates -log 10 (p-value) for association (y-axis) plotted against their respective log 2 (fold change) (x-axis). The dashed line represents significance level using a Bonferroni correction.
Figure 2.
Figure 2.
Forest plot comparing individual concepts between COVID-19-positive (case) and COVID-19-negative (control) patients. Selected associations include the significant signals related to semantic types of symptoms that met Bonferroni-corrected significance (p-value < 2.55E-06). The odds ratio has been adjusted for age, gender, and race. The concepts are ordered by p-value.
Figure 3.
Figure 3.
Temporal ConceptWAS using bi-weekly cumulative data. For significant signals (related to signs, symptoms) using all data (labeled in Figure 2), the plot indicates their −log 10 (p-value) for association (y-axis) against using the cumulative data started between March 8, 2020 to n weeks (x-axis). The dashed line indicates a significant association using a Bonferroni correction.
See this image and copyright information in PMC

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